方法对比
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| 域自适应卷积神经网络× | 基于卷积神经网络的迁移学习× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015–2017 | 2010–2014 |
| 提出者≠ | Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA) | Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al. |
| 类型≠ | Domain-adaptive deep learning model | Transfer learning applied to convolutional neural networks |
| 开创性文献≠ | Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 别名 | DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation | TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN |
| 相关≠ | 5 | 4 |
| 摘要≠ | A domain-adaptive CNN trains a convolutional network on a labeled source domain and adapts its learned feature representations to an unlabeled or lightly labeled target domain, bridging the distribution gap so that visual classifiers transfer reliably across datasets, sensors, or imaging conditions without full re-annotation. | Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch. |
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